Headlamp with an AI unit

ABSTRACT

A portable lamp, preferably a headlamp, which is adapted to be worn or carried by a user, comprising: at least one light source, an AI unit, wherein the AI unit comprises an activity classification unit and a control unit, wherein said activity classification unit is able to automatically classify an activity which the user is currently carrying out without any manual setting by the user, wherein said control unit is adapted to control the beam of said at least one light source at least based on the classified activity of the user.

CROSS REFERENCE TO RELATED APPLICATION

This application is a claims priority to EP 10 210 952.8 filed on Nov.22, 2019, the entire content of which is hereby incorporated byreference.

DESCRIPTION

The present invention relates to a portable lamp, preferably a headlampwhich is mainly used for sport and leisure outdoor activities and amethod for controlling at least one light source of such a portablelamp.

Portable lamp, especially headlamp, is one of the most useful andimportant gadgets for outdoor activities. A headlamp can usually be wornon the forehead of a user and is thus able to illuminate the key areawhile keeping the user's hands free.

For traditional headlamps, a user needs to manually set the geometry andbrightness of the lighting. However, in recent years, there has been agrowing tendency that headlamps become more and more electronicallycontrollable, wherein they become able to automatically adjust and adaptthe light beam to the environment.

However, it should be noted that the needed brightness and geometry ofthe light beam usually differ from one user activity to another. Forexample, the lighting mode for running should be different as thelighting mode for camping, as running requires a high proportion offocused beam of light for illuminating the way ahead, while campingneeds a high proportion of wide beam of light for enabling a broadvision of the environment and avoiding blinding other people nearby withfocused beam on their eyes.

Therefore, in order to produce a good lighting experience, some of suchheadlamps may comprise e.g. a USB port or a Bluetooth interface forconnection with a computer or a smartphone of the user, from where theuser could use an App to preset the lighting mode of the headlamp for aspecific sport or outdoor activity which the user is about to take,wherein each lighting mode has a specific profile or rules for automaticlighting adjustment.

Furthermore, each user may have a different preference or perception ofthe brightness or geometry of the light beam. In order to adapt thesetting of the lighting to the user preference, the user has toconfigure the adjusting parameters of each chosen activity manually orin an app on the computer or smartphone, which is not only timeconsuming, but also unintuitive, wherein a relative satisfied result cansometimes only be achieved after many iterations of configuring andtesting.

The objective of the present invention is thus to provide an improvedheadlamp and a method for controlling the lighting of such a headlamp,which address the above discussed disadvantages.

According to a first aspect of the present invention, this objective isachieved by a portable lamp, preferably a headlamp, which is adapted tobe worn or carried by a user, comprising: at least one light source, anAI unit, wherein the AI unit comprises an activity classification unitand a control unit, wherein said activity classification unit is able toautomatically classify an activity which the user is currently carryingout without any manual setting by the user, wherein said control unit isadapted to control the beam of said at least one light source at leastbased on the classified activity of the user.

AI refers to Artificial Intelligence, which is defined by John McCarthy,one of the founders of the discipline of AI, as “the science andengineering of making intelligent machines.” It should be noted thatunder this definition machine learning is a subset of AI, but AI doesnot need to necessarily have a machine learning capability, wherein ruleengines, expert systems and knowledge graphs could all be described asAI. The present application uses this definition, such that the AI unitrefers to unit capable of algorithm-based decision making, or anyintelligent unit with or without machine learning capabilities.

The at least one light source in the present application can be onesingle light source, which comprises e.g. a plurality of LED diodes,wherein the brightness and/or the geometry of the beam of the lightsource is controllable.

The headlamp could also comprise two or more light sources, wherein eachlight source has a specific geometry of beam, for example one lightsource with a focused beam, and another one with a wide beam, so thatthe headlamp can switch between these light sources to get a desired orsuitable beam geometry, wherein the brightness of each light source ispreferably controllable.

It is up to a person skilled in the art to implement the at least onelight source using any suitable type of controllable optics.

The AI unit comprises an activity classification unit, wherein theactivity classification unit is able to automatically classify anactivity which the user is currently carrying out without any manualsetting by the user. In this way, a pre-setting of a specific activityof the user using manual setting units like buttons, or an app on acomputer or a smartphone is not needed any more. Instead, the activityof the user will be automatically recognized by the activityclassification unit, which not only saves time of the user, but alsoenables a more intuitive and convenient user experience.

The AI unit further comprises a control unit. Based on the classifiedactivity, the algorithms or rules associated with this activity will beapplied for controlling the light source. The control unit uses thesecontrolling algorithms or rules to automatically control the beam of theat least one light source, including the geometry and the brightness ofthe beam.

In order to automatically classify the activity of the user or controlthe beam of light according to the environment, the features or themotions of the user and/or the nearby environment where the user istaking the activity need to be detected. For such detection, sensors areneeded. Thus, in a preferred embodiment, the lamp further comprises atleast one sensor, wherein the captured sensor data of the at least onesensor are adapted to be transmitted to the activity classification unitand/or to the control unit, wherein the at least one sensor comprises atleast one of the following: an inertial sensor, a GPS sensor, a compasssensor, a distance-to-objects sensor, a precision time tracker, anoptical detector such as an ambient light sensor.

An inertial sensor may comprise a combination of accelerometers andgyroscopes, which can be used for detecting a movement or an action ofthe user. An inertial sensor may be used to detect the speed anddirection of attention of the user while the user is carrying out anactivity. As a different sport activity usually has a different speedrange and head movement pattern, the detected speed and direction ofattention can be important parameters for identifying the activity.

A GPS sensor may be used to detect the geographic location of the userwho is wearing or carrying the head lamp. The geographic location mightbe associated with a specific activity, e.g. a famous ski area. Thus, aGPS sensor can be used to identify a user activity as well.

A compass sensor may measure the orientation of user compared to theearth magnetic field. This can for example be used to determine theangular orientation of the user at any time during his or her activity.Added to a precision time tracker (precise day time) the compassobtained orientation of user can be compared to the sunlight's potencyand orientation, helping to determine lighting needs.

A distance sensor may be used to detect the distance between the userand an object or a person, preferably based on the Time of Flight (ToF)principle, wherein the measurement is based on the time differencebetween the emission of a signal and its return to the sensor, afterbeing reflected by the object or the person. Different types of signalscan be used with the ToF principle, wherein the most common distancesensors are based on sound and light signals, e.g. ultrasonic sensors,Radar sensors, Lidar sensors, photo detectors in visible or infraredspectral regions and so on. Based on the distance information betweenthe user and the object, the beam of the light can be adjusted. Forexample, when a user who is camping starts to read a book or a map, thedistance sensor detects a nearby object, and will properly adjust thelighting beam or switch to a lighting beam which is suitable for closedistance actions such as reading.

A time tracking sensor such as a perennial watch may be used to recordthe absolute time as well as time period of usage. For example, aspecific place is a favorite sport destination for hiking in summer andskiing in winter, so that the time information can help to classify theuser activity. Furthermore, if a user has an everyday routine, e.g.walking after dinner at 8 O'clock in the evening, the time trackingsensor can also be used for training the AI unit and for identifying theuser habit accordingly.

An optical detector may be used for sensing ambient light. The controlunit can be adapted to control the brightness of the light beam based onenvironmental lighting conditions, e.g. a dark environment needs abright lighting beam, while in the sun the lighting is not needed anymore.

It should be noted that further sensors, other than the sensorsdiscussed above, which are suitable to detect a user action, a usermovement or the environment may also be used. It is up to a personskilled in the art to choose any suitable sensors to implement thepresent invention

Preferably, the activity classification unit is adapted to classify theactivity of the user as least based on the data transmitted from the atleast one sensor of the lamp. Further sensors, other than the introducedsensors above, can also be used, wherein the activity classificationunit is preferably adapted to use pre-determined or trained algorithmswhich compare the sensor data with pre-determined conditions orthresholds for identifying the user activity, or to develop rules oralgorithms with machine learning abilities for identifying the useractivity.

The control unit is preferably adapted to control the beam of the atleast one light source further based on the transmitted data from the atleast one sensor. The control unit can either use pre-determinedalgorithms or rules, which compare the sensor data with pre-determinedconditions or thresholds for identifying user actions or environmentconditions, or develop algorithms or rules using machine learning basedon the sensor data as training data.

In a preferred embodiment according the first aspect of the invention,the lamp preferably further comprises an AI on-off unit, wherein the AIon-off unit is adapted to be operated by the user to activate ordeactivate the AI unit. By providing such an AI on-off unit, the userhas the choice of whether or not to activate the AI unit. If a userwould like to completely manually set the lighting himself or use themanual setting for a specific time period, he could just operate the AIon-off unit by e.g. pressing a button to deactivate the AI unit. If at alater time he would like to use the AI unit again, what he needs to dois just to activate the AI unit.

Preferably, the lamp further comprises at least one manual setting unit,which is adapted to be operated by the user to manually control the beamof said at least one light source when the AI unit is deactivated,and/or is adapted to be operated by the user to further adjust the beamof said at least one light source when the AI unit is activated. The atleast one manual setting unit is preferably adapted to adjust thebrightness of the light and/or the geometry of the light. If a user hasdeactivated the AI unit, he may further use the at least one manualsetting unit to control the beam of the light source. Nevertheless, ifthe AI unit is activated, the user may still use the at least one manualsetting unit to adjust the beam of said at least one light source if heis not satisfied with the lighting result proposed by the AI unit, or hewould like to further carry out some fine-tuning of the light beam.

In a further preferred embodiment according to the first aspect of theinvention, the AI unit is preferably adapted to access preloadedtraining data, which are used to train the activity classification unitand/or the control unit. Preloaded training data may be training datawhich are provided by the manufacturer, wherein the manufacturer maycollect the training data from various test users before the lamp is putonto the market. In this way, algorithms for classifying user activitiesand/or algorithms for controlling the beam of the light source could betrained by the data collected from these test users, wherein themanufacturer, if time and cost permitted, could involve a lot of testusers of various ages and/or from various behavior groups, so that thetrained algorithms would be able to provide relative satisfying controlresults. A person skilled in the art may implement any kind ofalgorithms or learning models for classifying the user activity, whichcan include, inter alia, Convolutional Neural Networks, BayesianNetworks, Support Vector Machines or Decision Trees.

In order to better adapt the lamp to users with an individual preferenceor a different perception of the lighting, the AI unit preferablyfurther comprises a user-specific training data unit, which is adaptedto store user-specific control data resulted from user operations on theat least one manual setting unit, wherein the user specific control dataare adapted to train the control unit. The control data preferably referto the brightness and/or geometry of the beam of the at least one lightsource. No matter if the AI unit is activated or deactivated, the usercan always use the at least one manual setting unit to control the lightbeam. The control data of the manual setting, or the difference betweenthe proposed control data from the AI unit and the control data of themanual setting, will be stored and used for further training the controlalgorithms. By deploying this learning process, the AI unit will begradually adapted to the user-specific preference and would thus becomemore and more “intelligent”. Again, a person skilled in the art may alsoimplement any kind of algorithms or learning models for controlling thebeam of the at least one light source, which can include, inter alia,Convolutional Neural Networks, Bayesian Networks, Support VectorMachines or Decision Trees.

According to a second aspect of the invention, the object is achieved bya method for controlling at least one light source of a portable lamp,wherein the lamp is preferably a headlamp, which is adapted to be wornor carried by a user, comprising: classifying an activity which the useris currently carrying out without any manual setting by the user,controlling the beam of said at least one light source based on theclassified activity of the user.

As already discussed, with the automatic classification of a useractivity, a pre-setting of the activity by the user using an App on acomputer or a smartphone is not needed any more, which not only savestime of the user, but also enables a more intuitive and convenient userexperience. Based on the classified activity, the control unit usesactivity-specific profiles and controlling rules to automaticallycontrol the beam of the at least one light source, including thegeometry and the brightness of the beam, so that the control is not onlycompletely automatic but also activity-specific.

In a preferred embodiment according to the second aspect of theinvention, the lamp comprises an AI unit, wherein the AI unit comprisesan activity classification unit for classifying an activity of the userand a control unit for controlling the beam of said at least one lightsource, wherein the method further comprises training the classificationunit using preloaded training data.

Preferably, the lamp comprises an AI on-off unit, wherein the AI on-offunit is adapted to be operated by the user to activate or deactivate theAI unit.

The lamp preferably further comprises at least one manual setting unit,which is adapted to be operated by the user to manually control the beamof said at least one light source when the AI unit is deactivated,and/or is adapted to be operated by the user to further adjust the beamof the light source when the AI unit is activated.

According to a further preferred embodiment according to the secondaspect of the invention, the method further comprises: preloading datafor training at least one algorithm for classifying an activity of theuser, training the at least one algorithm for classifying an activity ofthe user using the preloaded data.

Preferably, the method further comprises: storing user-specific controldata resulted from user operations on the at least one manual settingunit, training at least one algorithm for controlling the beam of saidat least one light source using the user-specific control data.

The advantages of the above preferred embodiments according to thesecond aspect of the invention refer to the accordingly discussedadvantages under the first aspect of the invention, respectively.

According to a third aspect of the invention, the object is solved bythe use of an AI unit in a portable lamp, preferably a headlamp, whichis adapted to be worn or carried by a user, wherein the lamp comprisesat least one light source, wherein the AI unit is able to control thebeam of said at least one light source without any manual setting by theuser, wherein the headlamp further comprises at least one manual settingunit, which is adapted to be operated by the user to manually controlthe beam of said at least one light source when the AI unit isdeactivated, and/or is adapted to be operated by the user to furtheradjust the beam of said at least one light source when the AI unit isactivated, wherein at least one controlling algorithm of the AI unit forcontrolling the beam of said at least one light source is trained bypreloaded training data and/or user-specific control data resulted fromuser operations on the at least one manual setting unit.

According to the third aspect of the invention, the AI unit has alearning capability, wherein the at least one controlling algorithm ofthe AI unit may include, inter alia, Convolutional Neural Networks,Bayesian Networks, Support Vector Machines or Decision Trees, which canbe trained by user-specific control data resulted from user operationson the at least one manual setting unit.

By training the one or more algorithms of the AI unit with user-specificcontrol data, the AI unit learns the user preference or the user'sperception of light, so that not only an automatic but also anuser-specific control of the light beam is made possible, without theneed of a pre-configuration of a user profile by the user on a computeror a smartphone.

In a preferred embodiment according to the third aspect of theinvention, the lamp further comprises at least one sensor, wherein thecaptured sensor data of the at least one sensor are adapted to betransmitted to the AI unit, wherein the at least one sensor comprises atleast one of the following: an inertial sensor, a GPS sensor, a compasssensor, a distance sensor, a time tracking sensor, an optical detector.

Specific embodiments of the present invention will be described belowwith reference to the attached drawings in which

FIG. 1 shows a schematic view of a headlamp with an AI unit according toan embodiment of the invention.

FIG. 2 shows a schematic view of a headlamp with an AI unit according toa further embodiment of the invention.

FIG. 3 shows a process for controlling the beam of at least one lightsource of a head lamp according to the embodiment of FIG. 1 or FIG. 2 .

FIG. 4 shows a schematic view of a headlamp with an AI unit according toanother embodiment of the invention.

FIG. 5 shows a process for controlling the beam of at least one lightsource of a head lamp according to the embodiment of FIG. 4 .

FIG. 1 illustrates a schematic view of a headlamp with an AI unitaccording to a first embodiment of the invention. The headlamp 10comprises a power on-off unit 12, which is adapted to turn on or turnoff the power of the headlamp. The headlamp 10 further comprises twodimmable light sources 14 and 16, wherein each of them comprises one ormore LED diodes. The light source 14 may be adapted to produce a widebeam, and the light source 16 may be adapted to produce a focused beam.Each of the two light sources 14 and 16 is provided with a manualsetting unit 15 and 17, respectively, wherein each manual setting unit15 and 17 is adapted to be operated by the user to control thebrightness of the respective light source. It is up to a person skilledin the art how to implement the manual setting units 15 and 17, whereinthe manual setting units 15 and 17 could for example be implemented as abutton or a knob for adjusting the brightness of the light sources 14and 16, respectively.

The headlamp 10 may further comprise an AI unit 20, which is able toautomatically control the light sources 14 and 16 without any manualsetting of the user. The AI unit 20 comprises an activity classificationunit 22, which is able to automatically classify an activity which theuser is currently carrying out. The AI unit 20 further comprises acontrol unit 24, which is adapted to control the beam of the lightsources 14 and 16 based on the classified activity of the user. Anactivity of the user refers to a sport or a leisure outdoor activity,which can be hiking, camping, skiing, walking or running etc. Thepossible activities which the headlamp can be used for are usuallydefined or preset by the manufacturer.

In order to automatically classify the activity of the user, the motionsof the user and/or the nearby environment where the user is taking theactivity need to be detected. Therefore, the headlamp 10 furthercomprises one or more sensors 18 for such detection, wherein the one ormore sensors 18 comprise at least one of the following: an inertialsensor, a GPS sensor, a compass sensor, a distance sensor, a timetracking sensor, an optical detector.

The headlamp 10 further comprises an AI on-off unit 26, which is adaptedto be operated by the user to activate or deactivate the AI unit 20. TheAI on-off unit can also be implemented as a button or a knob. If a userdoes not want to use the AI unit 20, he may deactivate the AI unit 20 byoperating the AI on-off unit 26, so that he only uses the manual settingunits 15 and 17 to dim the light sources 14 and 16. However, even if theAI unit 20 is activated, the user can still use the manual setting units15 and 17 to further dim the light sources 14 and 16 if the user is notsatisfied with the proposed dimming results by the AI unit 20.

FIG. 2 shows a schematic view of a headlamp with an AI unit according toa further embodiment of the invention. Like the embodiment illustratedin FIG. 1 , the headlamp 100 in FIG. 2 also comprises a power on-offunit 112, a manual setting unit 115, a light source 114, an AI unit 120,one or more sensors 118 and preferably an AI on-off unit 126.

The only difference between the headlamps illustrated in FIG. 1 and FIG.2 is that the headlamp 100 in FIG. 2 has only one light source 114instead of two light sources 14 and 16. Furthermore, not only thebrightness, but also the geometry of the beam of the only one lightsource 114 may be adjustable, i.e. the light source 114 can be adjustedto have a wide beam, or a narrow beam, or a beam with any geometry. Thelight source 114 may comprise one or more LED diodes, wherein it is upto a person skilled in the art to implement any suitable optical systemsor technologies for the light beam adjustment. Accordingly, the AI unit120 as well as the manual setting unit 115 are not only able to controlthe brightness, but also the geometry of the beam of the light source114. The manual setting unit 115 can be implemented as two buttons, onefor adjusting the brightness of the light beam and the other foradjusting the geometry of the light beam. Furthermore, contactlessadjustment solutions e.g. by using hand gestures are also imaginable.For example, upon detection of moving the hand higher or lower in frontof the lamp via ToF technologies, the manual setting unit 115 adjuststhe brightness of the light source 114, while upon detection of movingthe hand farther or closer, the manual setting unit 115 adjusts therange of the light source 114.

A person skilled in the art could also use more than two light sourceson his needs, so that the number and the implementation of the lightsources as well as the according manual setting units may vary fromthose illustrated in FIG. 1 or FIG. 2 .

A process for controlling the beam of at least one light source of aheadlamp with an AI unit is shown in FIG. 3 , wherein the headlamp couldfor example be a headlamp of the embodiment shown in FIG. 1 or in FIG. 2. For simplicity's sake, in the following we use the embodimentillustrated in FIG. 2 for explaining the controlling process.

First of all, it should be noted that in FIG. 3 and FIG. 5 , arectangle-shaped box stands for an automatic process step, i.e. withoutany manual setting, while an oval-shaped box stands for a process stepwith a manual setting.

A user can operate on the power on-off unit 112 to turn on or turn offthe power of the headlamp 100. In embodiments that feature an AI on-offunit 26, once the headlamp is turned on, the user can further operate onthe AI on-off unit 26 to turn on or turn off the AI unit 120.

Once the AI unit 120 is turned on, the activity classification unit isadapted to read sensor data from at least one of the one or more sensors118.

Based on the sensor data, the activity classification unit 122 uses oneor more pre-determined algorithms to classify the activity the user iscurrently taking. The activity classification algorithms are preferablypre-trained by preloaded training data 130, wherein such training data130 are preferably provided by the manufacturer, who collects the datafrom test persons for training the algorithms. It should be noted thatthe activity classification unit 122 may still have access to thepreloaded training data 130 after the activity classification algorithmsare pre-trained, wherein the preloaded training data may further bysupplemented or updated with new data provided by the manufacturer forfurther training the algorithms. The preloaded training data 130 can bestored in the headlamp, or in an external data storage device, or in acloud which can be accessed by the activity classification unit 122.

Once the user activity is classified or determined, the control unit 124is adapted to use the according activity-specific control algorithm oralgorithms to control the beam of the light source 114. The one or morecontrol algorithms are also preferably pre-determined or pre-trained bythe manufacturer. It should be noted that the pre-loaded training data130 preferably further comprise training data for pre-training the oneor more control algorithms for each activity of the control unit 124.

In the following some examples for activity-specific control of the beamof the light source 114 are given. For example:

-   -   The one or more control algorithms for the activity “camping”        are adapted to mainly provide a wide lighting beam for enabling        a broad vision of the environment and avoiding blinding the eyes        of nearby persons when socializing, while the control algorithms        for the activity “cycling” or “running” are adapted to provide a        focused beam for illuminating the road ahead.    -   As the activity “cycling” has a higher speed than “walking”, the        one or more control algorithms for the activity “camping” should        be adapted to provide more lighting power, i.e. a higher        brightness of the light sources than those for the activity        “walking”.

It should be further noted that the one or more algorithms for useractivity classification should preferably run at a speed fast enough tocapture activity changes, such as the activity change from “walking” to“running”, preferably within one second, more preferably with afrequency of at least 24 Hz, which is the minimal visual detectioncapacity of human eyes.

However, under the same user activity, the lighting beam still needs tobe adjusted according to the environment or a movement or action of theuser. Therefore, the one or more control algorithms of the control unit124 for each activity are further adapted to control the beam of thelight source 114 based on the environment parameters, or an action or amovement of the user, which are measured or detected by the one or moresensors 118.

In the following some examples for light beam control under one sameuser activity 114 are given. For example:

-   -   The one or more control algorithms for the activity “running”        may be able to detect when the user stops running (e.g. lower        GPS-speed, lower cadence) and subsequently to reduce the        long-distance beam to avoid blinding the user because it is        probable that the user stops running for checking his cellphone        or a map.    -   Under the activity “skiing”, by detecting user movements uphill        and downhill, respectively, e.g. by using inertial sensors, the        one or more control algorithms for the activity “skiing” can be        adapted to set the beam stronger and with a mixed focus for        downhill, and to set the beam weaker but focused for uphill.    -   Head orientation (measured via inertial units) can also be an        important parameter for light beam controlling, wherein when the        user raises his head, he is probably looking at the landscape or        the sky, while when he lowers his head, he is probably looking        at the road in the immediate front. Therefore, the one or more        control algorithms for each activity can also be adapted to        adjust the light beam based on the detected head position of the        user.

Furthermore, as discussed above, the headlamp 100 also comprises amanual unit 115, which is operated by the user to manually adjust thebeam of the light source 114 when the AI unit 120 is deactivated, orwhen the AI unit is activated and the user is not completely satisfiedwith the proposed control result of AI unit.

The user-specific control data resulted from user operations on themanual setting unit 115 are stored as user-specific training data 132,which are used for further training the algorithms of the control unit124. The user-specific training data 132 can be control data of themanual setting, or the difference between the proposed control data fromthe AI unit and the control data of the manual setting, wherein theuser-specific training data 132 are preferably stored and/or updated inthe headlamp 100. In this way, the control unit 124 has the capabilityto learn from the manual settings of the user and is gradually adaptedto control the light beam according to the user preference. A personskilled in the art may implement any kind of algorithms or learningmodels for controlling the beam of the light source 114, which caninclude e.g. Convolutional Neural Networks, Bayesian Networks, SupportVector Machines or Decision Trees.

FIG. 4 illustrates another embodiment of the headlamp, wherein theheadlamp 200 also comprises a power on-off unit 212, which is adapted toturn on or turn off the power of the headlamp 200, an adjustable lightsource 214, and a manual setting unit 215, which is adapted to controlnot only the brightness but also the geometry of the respective lightsource 214. The headlamp 200 further comprises one or more sensors 218for detecting e.g. a user movement, a user action, or the environment.

The headlamp 200 further comprises an AI unit 220, which is able toautomatically control the beam of the light source 214 without anymanual setting of the user. The difference between the embodimentsillustrated in FIG. 2 and FIG. 4 is that the AI unit 200 in FIG. 4 onlycomprises a control unit 224, wherein a user activity classificationunit is not provided.

FIG. 5 illustrates a control process for controlling the beam of thelight source 214 of the headlamp 200. A user can also operate on thepower on-off unit 212 to turn on or turn off the power of the headlamp200. Once the headlamp is turned on, the user can further operate on theAI on-off unit 226 to turn on or turn off the AI unit 220.

Once the AI unit 220 is turned on, the control unit 224 is adapted toread sensor data from at least one of the one or more sensors 218.

Based on the sensor data, the control unit 224 is adapted to use one ormore control algorithms to control the beam of the light source 214. Theone or more control algorithms are preferably pre-determined, orpre-trained by pre-loaded training data 230 provided by themanufacturer, who collects the data from test persons for training theone or more algorithms. It should be noted that the control unit 224 maystill have access to the preloaded training data 230 after controlalgorithms are pre-trained, wherein the preloaded training data mayfurther by supplemented or updated with new data provided by themanufacturer for further training the one or more algorithms. Thepreloaded training data 230 can be stored in the headlamp, or in anexternal data storage device, or in a cloud which can be accessed by thecontrol unit 224.

In the following some examples for controlling the beam of the lightsource 214 are given. For example:

-   -   The control unit 224 is adapted to increase the brightness of        the beam of the light source 214, if the environment is very        dark, and to reduce the brightness of the beam of the light        source 214, if the environment is less dark, wherein the        darkness of the environment can be detected by an optical        detector.    -   The control unit 224 is adapted to switch the beam of light        source 214 from a focused beam to a wide beam, if a person stops        running and starts to read a map, wherein the action of the user        can e.g. be detected by a speed sensor, and the map, i.e. a        nearby object, can be detected by a distance sensor.

The user may further use the manual unit 215 to manually adjust the beamof the light source 214 when the AI unit 220 is deactivated, or when theAI unit 220 is activated and the user is not completely satisfied withthe proposed control result of AI unit, especially for the case that theuser has a different perception of the brightness of light than anaverage person.

The user-specific control data resulted from user operations on themanual setting unit 215 are stored as user-specific training data 232,which are used for further training the one or more algorithms of thecontrol unit 224. In this way, the control unit 224 has the capabilityto learn from the manual settings of the user and is gradually adaptedto control the light beam according to the user preference. A personskilled in the art may also implement any kind of algorithms or learningmodels for controlling the beam of the light source 214, which caninclude e.g. Convolutional Neural Networks, Bayesian Networks, SupportVector Machines or Decision Trees.

The invention claimed is:
 1. A wearable and head-mountable lightingapparatus having artificial intelligence, the wearable andhead-mountable lighting apparatus comprising: at least one light sourceconfigurable to direct at least one light beam away from a user of thewearable and head-mountable lighting apparatus; at least one sensorconfigured to capture sensor data, wherein the at least one sensorcomprises at least one of an inertial sensor, a GPS sensor, a compasssensor, a distance sensor, a time tracking sensor, or an opticaldetector; at least one processor and a storage device communicativelycoupled thereto, wherein the at least one processor is configured toprocess the sensor data using at least one algorithm to perform one ormore determinations comprising a position, orientation, distance from anobject, movement, timing, ambient light, or environmental state withrespect to the wearable and head-mountable lighting apparatus; anactivity classification unit configured to automatically classify, basedat least in part on the one or more determinations, an activity that theuser is carrying out while the user is wearing the wearable andhead-mountable lighting apparatus, without requiring manual input fromthe user to specify the activity; a control unit configured toautomatically control a geometry and a brightness of the at least onelight beam to be output by the at least one light source, based at leastin part on the activity of the user as automatically classified by theclassification unit, wherein, to automatically control the geometry andthe brightness of the at least one light beam, the at least one lightsource, the control unit, the activity classification unit, the at leastone processor, and the at least one sensor are further configured toautomatically adjust the brightness or the geometry of the at least onelight beam in response to a second change in the activity of the user asautomatically classified by the classification unit, following a firstchange in the sensor data captured by the at least one sensor, andwherein the activity classification unit is configured to accesspreloaded training data from the storage device, and wherein theactivity classification unit or the control unit is trained based atleast in part on the preloaded training data.
 2. The wearable andhead-mountable lighting apparatus according claim 1, wherein theactivity classification unit is further configured to automaticallyclassify the activity using at least one of a convolutional neuralnetwork, a Bayesian network, a support vector machine, or a decisiontree.
 3. The wearable and head-mountable lighting apparatus according toclaim 1, wherein the control unit is further configured to control thegeometry or the brightness of the at least one light beam of the atleast one light source, independently of the activity classificationunit, based at least in part on the sensor data.
 4. The wearable andhead-mountable lighting apparatus according to claim 1, furthercomprising an on-off unit configured to be operated by the user toactivate or deactivate the activity classification unit or the controlunit.
 5. The wearable and head-mountable lighting apparatus according toclaim 1, wherein the at least one light source further comprises atleast one manual setting unit configured to be operated by the user tomanually control the geometry or the brightness of the at least onelight beam of the at least one light source.
 6. The wearable andhead-mountable lighting apparatus according to claim 5, furthercomprising: a database stored in the storage device and configured tostore user-specific control data resulting from user operations on theat least one manual setting unit, wherein the activity classificationunit or the control unit is trained based at least in part on theuser-specific control data.
 7. The wearable and head-mountable lightingapparatus according to claim 5, wherein the at least one manual settingunit is configured to be operated by the user to adjust the geometry orthe brightness of the at least one light beam of the at least one lightsource when the activity classification unit or the control unit isdeactivated.
 8. The wearable and head-mountable lighting apparatusaccording to claim 1, wherein the control unit is further configured tocontrol the geometry or the brightness of the at least one light beam ofthe at least one light source, based at least in part on the sensordata.
 9. The wearable and head-mountable lighting apparatus according toclaim 1, wherein the activity classification unit is further configuredto automatically classify the activity within a minimal visual-detectiontime threshold of a human, and wherein the control unit is furtherconfigured to automatically control the geometry or the brightness ofthe at least one light beam of the at least one light source within onesecond of the first change.
 10. A method for automatically controllingat least one light source of a lighting apparatus, the at least onelight source being configurable to direct at least one light beam awayfrom a user of the lighting apparatus, the method comprising:classifying, by at least one processor included with the lightingapparatus, an activity that the user is carrying out while the user iswearing the lighting apparatus, based at least in part on sensor datareceived from at least one sensor included with the lighting apparatusand processed via the at least one processor using at least onealgorithm to perform one or more determinations comprising a position,orientation, distance from an object, movement, timing, ambient light,or environmental state, without requiring manual input from the user tospecify the activity, wherein the at least one sensor comprises at leastone of an inertial sensor, a GPS sensor, a compass sensor, a distancesensor, a time tracking sensor, or an optical detector; controlling, bythe at least one processor, a geometry and a brightness of the at leastone light beam to be output by the at least one light source, based atleast in part on the activity of the user as automatically classified bythe at least one processor, wherein the controlling the geometry and thebrightness of the at least one light beam further comprises: theclassifying, the controlling, the at least one light source, the atleast one processor, and the at least one sensor are further configuredto adjust the brightness or the geometry of the at least one light beamin response to a second change in the activity of the user as classifiedby the classification unit, following a first change in the sensor datacaptured by the at least one sensor; preloading training data into adatabase stored in a storage unit provided with the lighting apparatus;and training the at least one algorithm using the preloaded trainingdata.
 11. The method according to claim 10, wherein the at least onealgorithm uses an activity classification unit configured to classifythe activity of the user, and a control unit configured to control thegeometry or the brightness of the at least one light beam of the atleast one light source, and wherein the method further comprisestraining the activity classification unit using the preloaded trainingdata stored in the storage device included with the lighting apparatusand communicatively coupled with the at least one processor.
 12. Themethod according to claim 10, wherein the lighting apparatus comprisesat least one manual setting unit configured to be operated manually bythe user to control the geometry or the brightness of the at least onelight beam of said at least one light source, and wherein the methodfurther comprises: storing, by the at least one processor, in thestorage device included with the lighting apparatus, user-specificcontrol data resulting from user operations on the manual setting unit;and training, by the at least one processor, at least one controlalgorithm, wherein the training is based at least in part on theuser-specific control data, and wherein the at least one controlalgorithm is configured to control the geometry or the brightness of theat least one light beam of the at least one light source.
 13. The methodof claim 12, wherein the least one manual setting unit is configured tobe operated by the user to adjust the at least one light beam of the atleast one light source when the activity classification unit or thecontrol unit is deactivated.
 14. The method of claim 10, wherein each ofthe classifying and the controlling are performed at a rate within aminimal visual-detection time threshold of a human eye.
 15. A wearableand head-mountable lighting apparatus, the wearable and head-mountablelighting apparatus comprising: at least one light source configurable todirect at least one light beam away from a user of the wearable andhead-mountable lighting apparatus; at least one sensor configured tocapture sensor data, wherein the at least one sensor comprises at leastone of an inertial sensor, a GPS sensor, a compass sensor, a distancesensor, a time tracking sensor, or an optical detector; at least oneprocessor and a storage device communicatively coupled thereto, whereinthe at least one processor is configured to process the sensor datausing at least one algorithm to perform one or more determinationscomprising a position, orientation, distance from an object, movement,timing, ambient light, or environmental state with respect to thewearable and head-mountable lighting apparatus; an artificialintelligence (AI) unit configured to automatically control a geometryand a brightness of the at least one light beam to be output by the atleast one light source, based at least in part on the sensor data asprocessed using the at least one algorithm, without requiring manualinput from the user to specify the activity; at least one manual settingunit, configured to be operated by the user to manually control thegeometry or the brightness of the output of the at least one lightsource; at least one controlling algorithm executed by the AI unit andconfigured for controlling the geometry or the brightness of the outputof the at least one light source, wherein the at least one controllingalgorithm is configured to be trained using preloaded training data anduser-specific control data resulting from user operations on the atleast one manual setting unit, wherein, to automatically control thegeometry and the brightness of the at least one light beam, the at leastone light source, the controlling algorithm executed by the AI unit, theat least one processor, and the at least one sensor are furtherconfigured to automatically adjust the brightness or the geometry of theat least one light beam in response to a second change in the activityof the user as automatically classified by the classification unit,following a first change in the sensor data captured by the at least onesensor, and an activity classification unit configured to accesspreloaded training data from the storage device, and wherein theactivity classification unit or the AI unit is trained based at least inpart on the preloaded training data.
 16. The wearable and head-mountablelighting apparatus according to claim 15, wherein the least one manualsetting unit is configured to be operated by the user to adjust thegeometry or the brightness of the at least one light beam of the atleast one light source when the AI unit is deactivated.
 17. The wearableand head-mountable lighting apparatus according to claim 15, wherein theAI unit is further configured to automatically classify an activity thatthe user is carrying out while the user is wearing the wearable andhead-mountable lighting apparatus.
 18. The wearable and head-mountablelighting apparatus according to claim 15, wherein the at least oneprocessor is further configured to process the sensor data and performthe at least one controlling algorithm, to effect a second change in theat least one light beam of the light source, responsive to a firstchange in the sensor data, within one second of processing the sensordata corresponding to the first change in the sensor.